Abstract

Computer vision (CV) pipelines are typically eval-uated on datasets processed by image signal processing (ISP) pipelines. However, in order to deploy CV pipelines in resource-constrained devices, we need to avoid as many ISP steps as possible. In particular, most CV datasets consist of global shutter (GS) images even though most cameras today use a rolling shutter (RS). This paper studies the impact of different shutter mechanisms on machine learning (ML) object detection models using our novel Let's Roll dataset. This dataset consists of paired GS and RS images that we created using a combination of Unreal Engine 5 (UE5) and real snapshots we captured on the streets of downtown Los Angeles, spanning a variety of environments and lighting conditions. More specifically, we trained and evaluated mainstream object detection models with our paired GS and RS images to ascertain whether there exists a …

Metadata

publication

2024 IEEE Workshop on Signal Processing Systems (SiPS), 113-118, 2024

year

2024

publication date

2024/11/4

authors

Yue Hu, Gourav Datta, Kira Beerel, Yao Liu, Peter Beerel

link

https://ieeexplore.ieee.org/abstract/document/10768224/

conference

2024 IEEE Workshop on Signal Processing Systems (SiPS)

pages

113-118

publisher

IEEE